I have written some code…

analysis/first_script.R

library(tidyverse)
library(janitor)
library(ggbeeswarm)
library(here)

# Reading and cleaning data
penguins_df <- read_csv(here("data/penguins_raw.csv"), show_col_types = FALSE) |> 
  clean_names() |> 
  mutate(
    species = word(species, 1),
    year = year(date_egg),
    sex = str_to_lower(sex),
    year = as.integer(year),
    body_mass_g = as.integer(body_mass_g),
    across(where(is.character), as.factor)
  ) |> 
  select(
    species,
    island,
    year,
    sex,
    body_mass_g,
    bill_length_mm = culmen_length_mm,
    bill_depth_mm = culmen_depth_mm,
    flipper_length_mm
  ) |> 
  drop_na()

## Violin plot of body mass per species and sex
penguins_df |> 
  ggplot(aes(x = species, colour = sex, fill = sex, y = body_mass_g)) +
  geom_violin(alpha = 0.3, scale = "width") +
  geom_quasirandom(dodge.width = 0.9) +
  scale_colour_brewer(palette = "Set1") +
  scale_fill_brewer(palette = "Set1") +
  theme_minimal()

## Violin plot of flipper length per species and sex
penguins_df |> 
  ggplot(aes(x = species, colour = sex, fill = sex, y = flipper_length_mm)) +
  geom_violin(alpha = 0.3, scale = "width") +
  geom_quasirandom(dodge.width = 0.9) +
  scale_colour_brewer(palette = "Set1") +
  scale_fill_brewer(palette = "Set1") +
  theme_minimal()

## Scatter plot of bill length vs depth, with species and sex
penguins_df |> 
  ggplot(aes(x = bill_length_mm, y = bill_depth_mm, colour = species, shape = sex)) +
  geom_point() +
  scale_colour_brewer(palette = "Set2") +
  theme_minimal()

I have written some code…

Good start, but often:

Step 1: Turn your code into functions

# Reading and cleaning data
penguins_df <- read_csv(
  here("data/penguins_raw.csv"), 
  show_col_types = FALSE
) |> 
  clean_names() |> 
  mutate(
    species = word(species, 1),
    year = year(date_egg),
    sex = str_to_lower(sex),
    year = as.integer(year),
    body_mass_g = as.integer(body_mass_g),
    across(where(is.character), as.factor)
  ) |> 
  select(
    ## all relevant columns
  ) |> 
  drop_na()

read_data <- function(file) {
  readr::read_csv(file, show_col_types = FALSE) |> 
  janitor::clean_names() |> 
  dplyr::mutate(
    species = stringr::word(species, 1),
    year = lubridate::year(date_egg),
    sex = stringr::str_to_lower(sex),
    year = as.integer(year),
    body_mass_g = as.integer(body_mass_g),
    dplyr::across(
      dplyr::where(is.character), 
      as.factor
    )
  ) |> 
  dplyr::select(
    ## all relevant columns
  ) |> 
  tidyr::drop_na()
}

Step 1: Turn your code into functions

Don’t forget to document your functions! ({roxygen}-style)

#' Read and clean data
#' 
#' Reads in the penguins data, renames and selects relevant columns. The
#' following transformations are applied to the data: 
#' * only keep species common name
#' * extract observation year
#' * remove rows with missing values
#' 
#' @param file Character, path to the penguins data .csv file.
#' @returns A tibble.
read_data <- function(file) {
  readr::read_csv(file, show_col_types = FALSE) |> 
  janitor::clean_names() |> 
  dplyr::mutate(
    ## modifying columns
  ) |> 
  dplyr::select(
    ## all relevant columns
  ) |> 
  tidyr::drop_na()
}

Step 1: Turn your code into functions

Improved script:

R/helper_functions.R

#' Read and clean data
#' 
#' ...
read_data <- function(file) { ... }

#' Violin plot of variable per species and sex
#' 
#' ...
violin_plot <- function(df, yvar) { ... }

#' Scatter plot of bill length vs depth
#' 
#' ...
plot_bill_length_depth <- function(df) { ... }

analysis/first_script.R

library(here)

source(here("R/helper_functions.R"))

penguins_df <- read_data(
  here("data/penguins_raw.csv")
)

body_mass_plot <- violin_plot(
  penguins_df, 
  body_mass_g
)

flipper_length_plot <- violin_plot(
  penguins_df, 
  flipper_length_mm
)

bill_scatterplot <- plot_bill_length_depth(
  penguins_df
)

Step 2: Turn your main script into a {targets} pipeline!

From:

library(here)

source(here("R/helper_functions.R"))

penguins_df <- read_data(here("data/penguins_raw.csv"))

body_mass_plot <- violin_plot(penguins_df, body_mass_g)

flipper_length_plot <- violin_plot(penguins_df, flipper_length_mm)

bill_scatterplot <- plot_bill_length_depth(penguins_df)

Step 2: Turn your main script into a {targets} pipeline!

To:

library(targets)
library(here)

source(here("R/helper_functions.R"))

list(
  tar_target(penguins_raw_file, here("data/penguins_raw.csv"), format = "file"),
  
  tar_target(penguins_df, read_data(penguins_raw_file)),

  tar_target(body_mass_plot, violin_plot(penguins_df, body_mass_g)),

  tar_target(flipper_length_plot, violin_plot(penguins_df, flipper_length_mm)),

  tar_target(bill_scatterplot, plot_bill_length_depth(penguins_df))
)

Step 2: Turn your main script into a {targets} pipeline!

Aside: where should my targets script live?

targets::tar_config_set(script = "analysis/_targets.R", store = "analysis/_targets")

_target.yaml

main:
  script: analysis/_targets.R
  store: analysis/_targets

Visualise your pipeline

In the console, run:

targets::tar_visnetwork()

Execute your pipeline

In the console, run:

targets::tar_make()

here() starts at C:/Users/hrpoab/Desktop/GitHub/palmerpenguins_analysis
> dispatched target penguins_raw_file
o completed target penguins_raw_file [0 seconds]
> dispatched target penguins_df
o completed target penguins_df [0.85 seconds]
> dispatched target body_mass_plot
o completed target body_mass_plot [0.16 seconds]
> dispatched target bill_scatterplot
o completed target bill_scatterplot [0.02 seconds]
> dispatched target flipper_length_plot
o completed target flipper_length_plot [0.02 seconds]
> ended pipeline [1.31 seconds]

Get the pipeline results

targets::tar_read(bill_scatterplot)

Change in a step

Hi Olivia,

Great work! Just a minor comment, could you change the colours in the bill length/depth scatter-plot? It’s hard to see the difference between the species.

R/helper_functions.R

plot_bill_length_depth <- function(df) {
  df |> 
    ggplot2::ggplot(
      ggplot2::aes(
        x = bill_length_mm, 
        y = bill_depth_mm, 
        colour = species, 
        shape = sex
        )
    ) +
    ggplot2::geom_point() +
    ggplot2::scale_colour_brewer(palette = "Set2") +
    ggplot2::theme_minimal()
}

Change in a step

Hi Olivia,

Great work! Just a minor comment, could you change the colours in the bill length/depth scatter-plot? It’s hard to see the difference between the species.

R/helper_functions.R

plot_bill_length_depth <- function(df) {
  df |> 
    ggplot2::ggplot(
      ggplot2::aes(
        x = bill_length_mm, 
        y = bill_depth_mm, 
        colour = species, 
        shape = sex
        )
    ) +
    ggplot2::geom_point() +
    ggplot2::scale_colour_brewer(palette = "Set1") +
    ggplot2::theme_minimal()
}

Change in a step

targets::tar_visnetwork()

Change in a step

targets::tar_make()

here() starts at C:/Users/hrpoab/Desktop/GitHub/palmerpenguins_analysis
v skipped target penguins_raw_file
v skipped target penguins_df
v skipped target body_mass_plot
> dispatched target bill_scatterplot
o completed target bill_scatterplot [0.35 seconds]
v skipped target flipper_length_plot
> ended pipeline [0.53 seconds]

Change in a step

targets::tar_read(bill_scatterplot)

Change in the data

Hi Olivia,

Oopsie! We realised there was a mistake in the original data file. Here is the updated spreadsheet, could you re-run the analysis with this version?

targets::tar_visnetwork()

Change in the data – using {assertr} for data checking

Problem: a change in the data format (column names or format, range of values, etc) might cause the pipeline to fail

Using {assertr} for data checking

read_data <- function(file) {
  readr::read_csv(file, show_col_types = FALSE) |> 
    janitor::clean_names() |> 
    ## data cleaning code
}

Using {assertr} for data checking

read_data <- function(file) {
  readr::read_csv(file, show_col_types = FALSE) |> 
    janitor::clean_names() |> 
    assertr::verify(
      assertr::has_all_names(
        "species", "island", "date_egg", "sex",
        "body_mass_g", "culmen_length_mm",
        "culmen_depth_mm", "flipper_length_mm"
      )
    ) |>
    ## data cleaning code
}

Using {assertr} for data checking

read_data <- function(file) {
  readr::read_csv(file, show_col_types = FALSE) |> 
    janitor::clean_names() |> 
    assertr::verify(
      assertr::has_all_names(
        "species", "island", "date_egg", "sex",
        "body_mass_g", "culmen_length_mm",
        "culmen_depth_mm", "flipper_length_mm"
      )
    ) |>
    assertr::assert(rlang::is_integerish, body_mass_g) |>
    ## data cleaning code
}

Using {assertr} for data checking

read_data <- function(file) {
  readr::read_csv(file, show_col_types = FALSE) |> 
    janitor::clean_names() |> 
    assertr::verify(
      assertr::has_all_names(
        "species", "island", "date_egg", "sex",
        "body_mass_g", "culmen_length_mm",
        "culmen_depth_mm", "flipper_length_mm"
      )
    ) |>
    assertr::assert(rlang::is_integerish, body_mass_g) |>
    ## data cleaning code
    assertr::verify(flipper_length_mm > 0)
}

Writing a report with Quarto

reports/palmerpenguins_report.qmd

---
title: "Analysis of penguins measurements from the palmerpenguins dataset"
author: "Olivia Angelin-Bonnet"
date: today
format:
  docx:
    number-sections: true
---

```{r setup}
#| include: false

library(knitr)

opts_chunk$set(echo = FALSE)
```

This project aims at understanding the differences between the size of three species of penguins (Adelie, Chinstrap and Gentoo) observed in the Palmer Archipelago, Antarctica, using data collected by Dr Kristen Gorman between 2007 and 2009.

## Distribution of body mass and flipper length

@fig-body-mass shows the distribution of body mass (in grams) across the three penguins species. We can see that on average, the Gentoo penguins are the heaviest, with Adelie and Chinstrap penguins more similar in terms of body mass. Within a species, the females are on average lighter than the males.

```{r fig-body-mass}
#| fig-cap: "Distribution of penguin body mass (g) across species and sex."

# code for plot
```

Similarly, Gentoo penguins have the longest flippers on average (@fig-flipper-length), and Adelie penguins the shortest. Again, females from a species have shorter flippers on average than the males.

```{r fig-flipper-length}
#| fig-cap: "Distribution of penguin flipper length (mm) across species and sex."

# code for plot
```


## Association between bill length and depth

In this dataset, bill measurements refer to measurements of the culmen, which is the upper ridge of the bill. There is a clear relationship between bill length and depth, but it is masked in the dataset by differences between species (@fig-bill-scatterplot), with Gentoo penguins exhibiting longer but shallower bills, and Adelie penguins shorter and deeper bills.

```{r fig-bill-scatterplot}
#| fig-cap: "Scatterplot of penguin bill length and depth."

# code for plot
```

Writing a report – Quarto + {targets}

Two advantages of using a Quarto document alongside {targets}:

Writing a report – Quarto + {targets}

reports/palmerpenguins_report.qmd

---
title: "Analysis of penguins measurements from the palmerpenguins dataset"
author: "Olivia Angelin-Bonnet"
date: today
format: docx
---

```{r setup}
#| include: false

library(knitr)

opts_chunk$set(echo = FALSE)
```

This project aims at understanding the differences...

## Distribution of body mass and flipper length

@fig-body-mass shows...

```{r fig-body-mass}
#| fig-cap: "Distribution of penguin body mass (g) across species and sex."

# code for plot
```

Writing a report – Quarto + {targets}

reports/palmerpenguins_report.qmd

---
title: "Analysis of penguins measurements from the palmerpenguins dataset"
author: "Olivia Angelin-Bonnet"
date: today
format: docx
---

```{r setup}
#| include: false

library(knitr)
library(here)

opts_chunk$set(echo = FALSE)
opts_knit$set(root.dir = here())
```

This project aims at understanding the differences...

## Distribution of body mass and flipper length

@fig-body-mass shows...

```{r fig-body-mass}
#| fig-cap: "Distribution of penguin body mass (g) across species and sex."

# code for plot
```

Writing a report – Quarto + {targets}

reports/palmerpenguins_report.qmd

---
title: "Analysis of penguins measurements from the palmerpenguins dataset"
author: "Olivia Angelin-Bonnet"
date: today
format: docx
---

```{r setup}
#| include: false

library(knitr)
library(here)
library(targets)

opts_chunk$set(echo = FALSE)
opts_knit$set(root.dir = here())
```

This project aims at understanding the differences...

## Distribution of body mass and flipper length

@fig-body-mass shows...

```{r fig-body-mass}
#| fig-cap: "Distribution of penguin body mass (g) across species and sex."

tar_read(body_mass_plot)
```

Writing a report – Quarto + {targets}

Adding the Quarto report as a step in the pipeline (need {tarchetypes} and {quarto}):

analysis/_targets.R

library(targets)
library(tarchetypes)
library(here)

source(here("R/helper_functions.R"))

list(
  tar_target(
    penguins_raw_file, 
    here("data/penguins_raw.csv"), 
    format = "file"
  ),
  tar_target(
    penguins_df, 
    read_data(penguins_raw_file)
  ),
  # etc
  tar_quarto(
    report, 
    here("reports/palmerpenguins_report.qmd")
  )
)

{targets} – how to reproduce the analysis?

README.md

## How to reproduce the analysis

```{r}
# Install the necessary packages
renv::restore()

# Run the analysis pipeline
targets::tar_make()
```

Presentation disclaimer